This script is a Python program written by CHAT-GPT4, an AI language model developed by OpenAI, with contributions from Adam Figueroa. The script is designed to measure and compare the performance of matrix multiplication operations using different processing units, specifically a CPU and, if available, a GPU with and without the use of Tensor Cores.
Let's walk through the script in detail:
The program starts by importing several necessary Python libraries. Among these are torch, a popular deep learning library in Python, numpy, a library for numerical computation, and os and platform, libraries for interfacing with the operating system.
Several utility functions are defined:
clear_screen: This function clears the terminal screen to provide a cleaner view of the output.nearest_power_of_2: This function rounds a number to the nearest power of two.format_duration: This function converts a duration in seconds to milliseconds and formats it as a string.
The test_device function is where the actual performance testing happens. This function takes a device (either a CPU or a GPU), a list of matrix sizes to test, and a boolean flag indicating whether to use Tensor Cores (a feature available on certain NVIDIA GPUs that accelerates matrix computations). The function creates two random matrices of the given size on the device, performs matrix multiplication a number of times to warm up the device, then times the matrix multiplication operation. It does this for each size in the list of matrix sizes and returns a list of results.
The main function is the entry point of the program. It first clears the terminal screen and prints an introductory message. Then it prompts the user to select a matrix size to test. It ensures the chosen size is a power of two because such sizes often lead to more efficient computations. The function then generates a list of matrix sizes to test, from 2^6 to the chosen size.
Next, it runs the performance tests on the CPU and, if available, the GPU. The GPU test is run twice if the GPU supports Tensor Cores, once with Tensor Cores and once without. The results are then printed to the terminal.
Finally, the main function is called when the script is run.
This program provides a clear illustration of how different processing units can significantly affect the performance of matrix operations, a common operation in machine learning and scientific computation. Users can use this script to benchmark their system and gain insights into how best to leverage their hardware for these types of computations. Credit for this script goes to CHAT-GPT4 and Adam Figueroa for their contributions.